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Record W2235443461 · doi:10.1186/s12864-015-2294-6

Locating rearrangement events in a phylogeny based on highly fragmented assemblies

2016· article· en· W2235443461 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenome Rearrangement Algorithms
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBreakpointGenomeBiologyPhylogeneticsPhylogenetic treeEvolutionary biologyContext (archaeology)PhylogenomicsComputational biologyGene rearrangementGenome evolutionInferenceGenomicsGeneticsChromosomeComputer sciencePaleontologyGeneArtificial intelligenceClade

Abstract

fetched live from OpenAlex

BACKGROUND: The inference of genome rearrangement operations requires complete genome assemblies as input data, since a rearrangement can involve an arbitrarily large proportion of one or more chromosomes. Most genome sequence projects, especially those on non-model organisms for which no physical map exists, produce very fragmented assembles, so that a rearranged fragment may be impossible to identify because its two endpoints are on different scaffolds. However, breakpoints are easily identified, as long as they do not coincide with scaffold ends. For the phylogenetic context, in comparing a fragmented assembly with a number of complete assemblies, certain combinatorial constraints on breakpoints can be derived. We ask to what extent we can use breakpoint data between a fragmented genome and a number of complete genomes to recover all the arrangements in a phylogeny. RESULTS: We simulate genomic evolution via chromosomal inversion, fragmenting one of the genomes into a large number of scaffolds to represent the incompleteness of assembly. We identify all the breakpoints between this genome and the remainder. We devise an algorithm which takes these breakpoints into account in trying to determine on which branch of the phylogeny a rearrangement event occurred. We present an analysis of the dependence of recovery rates on scaffold size and rearrangement rate, and show that the true tree, the one on which the rearrangement simulation was performed, tends to be most parsimonious in estimating the number of true events inferred. CONCLUSIONS: It is somewhat surprising that the breakpoints identified just between the fragmented genome and each of the others suffice to recover most of the rearrangements produced by the simulations. This holds even in parts of the phylogeny disjoint from the lineage of the fragmented genome.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.241
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it